Annotation Data Analysis

# Importing libraries
import yaml
import pymongo
import pandas as pd
from collections import Counter

# Import plot libraries
import plotly.express as px

Util functions

# File function - Read dict from YAML file
def get_dict_from_yaml(yaml_path:str, encoding:str="utf-8") -> dict:
    result = dict()
    
    try:
        with open(yaml_path, mode="r", encoding=encoding) as file:
            yaml_file = file.read()
            result = yaml.load(yaml_file, Loader=yaml.FullLoader)
    except Exception as e:
        print(e)
        
    return result

def string_to_list(str_list:str) -> list:
    if str_list == "[]":
        return []
    str_list = str_list.replace("[", "").replace("]", "").replace(" ", "").replace("\n", "")
    return str_list.split(",")

# Util function - Plot column chart
def plot_bar_chart(df, x_var, y_var, title) -> None:
    fig = px.bar(df, x=x_var, y=y_var, orientation='h', title=title)
    fig.update_layout(yaxis={'categoryorder':'total ascending'}, showlegend=True)
    fig.show()

Read data

# Read MongoDB login info
filepath = "config/mdb_setup.yaml"
mdb_login = get_dict_from_yaml(filepath)

# Create connetion to MongoDB
client  = pymongo.MongoClient(mdb_login['db_server'], mdb_login['db_port'])
db = client[mdb_login['db_name']]
coll = db[mdb_login['db_collection']]

# Query data
doc_list = []
for doc in coll.find({}):
    doc['claim']['nouns'] = string_to_list(doc['claim']['nouns'])
    doc['claim']['entities'] = string_to_list(doc['claim']['entities'])
    doc['majorClaim']['nouns'] = string_to_list(doc['majorClaim']['nouns'])
    doc['majorClaim']['entities'] = string_to_list(doc['majorClaim']['entities'])
    doc['premise']['nouns'] = string_to_list(doc['premise']['nouns'])
    doc['premise']['entities'] = string_to_list(doc['premise']['entities'])
    doc_list.append(doc)

print(">> Number of annotated documents:", len(doc_list))

>> Number of annotated documents: 4702

doc_list[0]

{'_id': ObjectId('618315e580c699e1073e9a16'),
 'argumentID': '5-1',
 'approach': 'A2 -> C+(L+P)',
 'claim': {'text': 'Mi idea es dar un uso al arco de la victoria de Moncloa , en lugar de demolerlo , como soporte',
  'nouns': ['idea',
   'uso',
   'arco',
   'victoria',
   'lugar',
   'demolerlo',
   'soporte'],
  'entities': ['Moncloa']},
 'linker': {'linker': 'para',
  'category': 'CONSEQUENCE',
  'subCategory': 'GOAL',
  'relationType': 'support'},
 'mainVerb': 'dar',
 'majorClaim': {'text': 'Uso artistico y de promocion del  Arco de la Victoria',
  'nouns': ['Uso', 'promocion'],
  'entities': ['de']},
 'premise': {'text': 'promos de actividades en Madrid',
  'nouns': ['promos', 'actividades'],
  'entities': []},
 'proposalID': 5,
 'sentence': 'Mi idea es dar un uso al arco de la victoria de Moncloa, en lugar de demolerlo, como soporte para promos de actividades en Madrid (semana del orgullo gay, congresos mundiales, eventos futbol, etc)'}

Data Processing

# Local variables
n_top = 20
topic_filter = ""
categories = Counter()
sub_categories = Counter()
linkers = Counter()
relations = Counter()
entities = Counter()
nouns = Counter()
main_verbs = Counter()

# Counting data
for doc in doc_list:
    if doc["sentence"] == "" or topic_filter in doc["sentence"]:
        
        # Linker data
        doc_linker = doc["linker"]
        categories[doc_linker["category"]] += 1
        sub_categories[doc_linker["subCategory"]] += 1
        linkers[doc_linker["linker"]] += 1
        relations[doc_linker["relationType"]] += 1
        
        # Entities data
        entities_set = set(doc["claim"]["entities"] + doc["majorClaim"]["entities"] + doc["premise"]["entities"])
        for entity in entities_set:
            entities[entity] += 1
            
        # Nouns data
        nouns_set = set(doc["claim"]["nouns"] + doc["majorClaim"]["nouns"] + doc["premise"]["nouns"])
        for entity in nouns_set:
            nouns[entity] += 1
            
        # Verbs data
        if doc["mainVerb"] != None:
            main_verbs[doc["mainVerb"].lower()] += 1

1. Most used categories

# Cooking dataframe
top_categories = categories.most_common(n_top)
df = pd.DataFrame.from_records(top_categories, columns = ['categories', 'frequency'])
df["% share"] = round(100.0 * df["frequency"] / sum(categories.values()), 2)
df.head(10)

	categories	frequency	% share
0	CONSEQUENCE	3226	68.61
1	CAUSE	1189	25.29
2	EXPLANATION	167	3.55
3	CONTRAST	119	2.53
4	ELABORATION	1	0.02

# Plot top N most used categories
x_var = 'frequency'
y_var = 'categories'
title = 'Top %s Most used categories' % min(n_top, len(df))
plot_bar_chart(df, x_var, y_var, title)

2. Most common sub-categories

# Cooking dataframe
top_sub_categories = sub_categories.most_common(n_top)
df = pd.DataFrame.from_records(top_sub_categories, columns = ['subcategories', 'frequency'])
df["% share"] = round(100.0 * df["frequency"] / sum(sub_categories.values()), 2)
df.head(10)

	subcategories	frequency	% share
0	GOAL	3158	67.16
1	CONDITION	662	14.08
2	REASON	527	11.21
3	EXEMPLIFICATION	137	2.91
4	ALTERNATIVE	119	2.53
5	RESULT	68	1.45
6	RESTATEMENT	29	0.62
7	SUMMARY	1	0.02
8	ADDITION	1	0.02

# Plot top N most used sub-categories
x_var = 'frequency'
y_var = 'subcategories'
title = 'Top %s Most used sub-categories' % min(n_top, len(df))
plot_bar_chart(df, x_var, y_var, title)

3. Most used linkers

# Cooking dataframe
top_linkers = linkers.most_common(n_top)
df = pd.DataFrame.from_records(top_linkers, columns = ['linkers', 'frequency'])
df["% share"] = round(100.0 * df["frequency"] / sum(linkers.values()), 2)
df.head(10)

	linkers	frequency	% share
0	para	3115	66.25
1	si	445	9.46
2	ya que	228	4.85
3	porque	160	3.40
4	por ejemplo	90	1.91
5	ante	67	1.42
6	pues	49	1.04
7	ni	42	0.89
8	debido a	40	0.85
9	o	37	0.79

# Plot top N most used linkers
x_var = 'frequency'
y_var = 'linkers'
title = 'Top %s Most used linkers' % min(n_top, len(df))
plot_bar_chart(df, x_var, y_var, title)

4. Most common entities

# Cooking dataframe
top_entities = entities.most_common(n_top)
df = pd.DataFrame.from_records(top_entities, columns = ['entities', 'frequency'])
df["% share"] = round(100.0 * df["frequency"] / sum(entities.values()), 2)
df.head(10)

	entities	frequency	% share
0	Ayuntamiento	102	2.56
1	EMT	55	1.38
2	Vallecas	42	1.06
3	Rio	36	0.90
4	AyuntamientodeMadrid	32	0.80
5	Valdebebas	30	0.75
6	Moratalaz	30	0.75
7	M-30	28	0.70
8	Sanchinarro	28	0.70
9	de	27	0.68

# Plot top N most used entities
x_var = 'frequency'
y_var = 'entities'
title = 'Top %s Most used entities' % min(n_top, len(df))
plot_bar_chart(df, x_var, y_var, title)

5. Most common nouns

# Cooking dataframe
top_nouns = nouns.most_common(n_top)
df = pd.DataFrame.from_records(top_nouns, columns = ['nouns', 'frequency'])
df["% share"] = round(100.0 * df["frequency"] / sum(nouns.values()), 2)
df.head(10)

	nouns	frequency	% share
0	calle	367	1.35
1	zona	336	1.24
2	calles	259	0.95
3	barrio	258	0.95
4	personas	216	0.79
5	parque	212	0.78
6	ciudad	210	0.77
7	uso	189	0.70
8	transporte	189	0.70
9	zonas	186	0.68

# Plot top N most used nouns
x_var = 'frequency'
y_var = 'nouns'
title = 'Top %s Most used nouns' % min(n_top, len(df))
plot_bar_chart(df, x_var, y_var, title)

6. Most common main verbs

# Cooking dataframe
top_main_verbs = main_verbs.most_common(n_top)
df = pd.DataFrame.from_records(top_main_verbs, columns = ['main verbs', 'frequency'])
df["% share"] = round(100.0 * df["frequency"] / sum(main_verbs.values()), 2)
df.head(10)

	main verbs	frequency	% share
0	propongo	184	4.23
1	crear	171	3.94
2	poner	82	1.89
3	hacer	66	1.52
4	ampliar	62	1.43
5	creo	54	1.24
6	tiene	52	1.20
7	gustaria	48	1.10
8	habilitar	47	1.08
9	tienen	46	1.06

# Plot top N most used main verbs
x_var = 'frequency'
y_var = 'main verbs'
title = 'Top %s Most used main verbs' % min(n_top, len(df))
plot_bar_chart(df, x_var, y_var, title)

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